arcosoph/AE29H_float32

AE29H_float32

Audio Embeddings ~29 hours dataset contains precomputed audio embeddings designed for [Nanowakeword](https://github.com/arcosoph/nanowakeword) framework. The embeddings are intended to be used as general-purpose negative training data, meaning the audio does not contain the target wake word or phrase.

Unlike raw audio datasets, the files in this dataset contain low-dimensional audio embeddings extracted from audio clips using a pre-trained speech embedding model. These embeddings can be directly used as input features when training wake-word detection models with NanoWakeWord.

The goal of this dataset is to provide diverse background audio representations (speech, environmental noise, music, etc.) that help wake-word models learn to avoid false activations.

Dataset Source

The embeddings were generated from a subset of the [ACAV100M](https://acav100m.github.io/) dataset.

ACAV100M is a very large automatically curated audio-visual dataset created from millions of internet videos and designed for large-scale audio-visual learning. It contains diverse real-world audio such as speech, environmental sounds, music, and background noise.

For this dataset:

• —A 20K subset (~2 days of audio) from ACAV100M.
• —Audio clips were processed and converted into embeddings suitable for wake-word training.

Dataset Statistics

• —Shape:

  (21115, 16, 96)

• —Total samples: 21,115

• —Feature dimensions:

• —Temporal steps: 16
• —Embedding size: 96 Each sample represents approximately 1.28 seconds of audio, where each temporal step corresponds to ~80 ms.

Data Type

dtype: float32

Value range

min: -77.23914
max: 95.59355

Intended Use

This dataset is intended for:

• —Training NanoWakeWord wake-word detection models
• —Providing negative training examples
• —Improving false-positive robustness
• —Training models that operate directly on audio embeddings